qqgjyx 0.1.5

Personal utility toolkit

qqgjyx

Personal utility toolkit for ML/DL workflows. Lightweight, modular, and designed for personal projects. Ships a clean, sklearn-inspired structure and a unified QQ interface for ergonomics.

Installation

pip install qqgjyx
# or pin a version
pip install qqgjyx==0.1.2

Requires Python 3.9+.

Highlights

• Concise, cohesive API with the QQ class
• Environment and device utilities
• Reproducibility helpers (seeding)
• Matplotlib styling (scienceplots)
• Dataset helpers (train/val split)
• Sklearn-like structure for long-term maintainability

Quick start

from qqgjyx import QQ

QQ.help()      # Show available utilities and usage
QQ.env()       # Print environment info
QQ.dev()       # Print device info and return torch.device
QQ.seed(42)    # Set seeds across numpy/torch/lightning
QQ.style()     # Set matplotlib style (scienceplots)

# Example: split a Dataset into train/val
from torch.utils.data import Dataset

class MyDataset(Dataset):
    ...

train_set, val_set = QQ.split(MyDataset(...), val_ratio=0.2, seed=42)

Flat, concise functions (direct imports)

from qqgjyx.helper import env, dev, seed
from qqgjyx.visual import style
from qqgjyx.data import split

env()
device = dev()
seed(123)
style()

Backwards-compatible aliases are available:

• print_environment_info() → env()
• get_device_info() → dev()
• set_all_seeds() → seed()
• set_plt_style() → style()
• train_val_split() → split()

Module layout

qqgjyx/
  __init__.py      # version and QQ export
  qq.py            # QQ class (unified interface)
  helper.py        # env(), dev(), seed()
  data/
    __init__.py    # split()
    split.py
  visual/
    __init__.py    # style()
    plotting.py
  model/           # placeholder
  graph/           # placeholder
  validator.py     # ensure_between()
  exceptor.py      # QQGJYXError

Demo notebook

See demo.ipynb at the repository root for a walk-through of all features.

Development

Recommended: use a conda env (example: pkg-dev).

conda run -n pkg-dev python -m pip install -e .
conda run -n pkg-dev python -m pytest -q

Release pipeline (script)

Use the automated script to version, test, build, upload, and tag.

python deploy.py --version 0.1.3 --message "Add new feature"

# or auto-increment patch version
python deploy.py

# options
python deploy.py --skip-tests
python deploy.py --skip-upload
python deploy.py --dry-run

Under the hood, it:

• updates versions in src/qqgjyx/__init__.py and pyproject.toml
• runs tests, builds sdist/wheel, validates with twine
• uploads to PyPI (using local credentials)
• commits and tags the release

Testing

conda run -n pkg-dev python -m pytest test/ -v

The suite includes core tests that avoid heavy deps and optional comprehensive tests (with mocks).

License

MIT License. See LICENSE.